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Nurturing Environment through Data Science

Maira Masood
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Nurturing Environment through Data Science

Earth, a habitable planet that supports millions of lives, is what we all have in common. Doubtlessly, this planet is a blessing worth fighting for. However, the rate at which we are deteriorating Earth is an eye-opener. Though the figures may not sound highly deleterious, in reality, we are destroying our planet faster than we can process. Even though we claim to be advanced enough to be stepping into the 21st century and producing innovative ideas and solutions to facilitate humankind, we need to focus on grassroots and protect our environment first.

“What’s the use of a fine house if you haven’t got a tolerable planet to put it on.”  – Henry David Thoreau

Statistics reveal astonishing facts about environmental degradation. According to the World Health Organization (WHO), climate change is estimated to cause 250,000 added multifactorial deaths per year due to malaria, malnutrition, diarrhea, and heat stress between 2030 and 2050. 7,000,000 casualties have occurred due to air pollution, as stated by WHO (2016). Radical fluctuations in rainfalls have resulted in many potent issues such as floods, landslides, and droughts. Considering the current situation and drastic climatic shifts, the world will be hit by several episodes of natural disasters at the expense of numerous lives in no time.

Infinite inventions and discoveries are seen under progress across fields such as astronomy, genetics, and mechanics, all striving towards a common goal: Facilitate humanity in daily life. If the uprising technological developments can facilitate human activities greatly, they can surely aid humankind in protecting the environment. Maybe data science is the solution to these environmental affairs.

An interdisciplinary field revolutionizing the modern world, data science applies algorithms, computational tools, and machine learning techniques to extract useful information from the available raw data. The data that is worked on can be sourced from multiple channels and be in different formats. It allows rapid processing of data, enabling storage and quick retrieval of a humongous amount of data. Data science is a workflow phenomenon that involves five basic steps to obtain the desired results, as depicted in the picture below.

Data science can offer several benefits to humankind concerning environmental protection that can help us better strategize, frame measures and action plans to minimize natural disasters, protect wildlife and sustain the environment for present and future generations.

It is paramount to understand the critical aspects of the environment and how nature works to precisely utilizing the complex technology to our utmost benefit. This will help us understand how nature and natural processes, in turn, affect human health, food availability, resource exploitation, and influence human activities.

Air Pollution

One of the several issues mankind is facing for decades is air pollution. The air quality has dropped significantly over the years, and access to clean, fresh air is a hurdle. This effect is pronounced in urban areas where forest density is diminishing with the blink of the eye; automobile usage has shown a rapid increase in health-associated conditions such as chronic and acute lung diseases, respiratory disorders, and heart diseases. 

System (MCMS) uses sensors and software to instantly measure air quality in real-time, which provides meaningful data. 

It works by producing microclimatic data measurements for EPA’ criteria pollutants’ including carbon monoxide, carbon dioxide, nitric oxide, nitrogen dioxide, and Sulphur dioxide. It also provides provisions for measurements of temperature, relative humidity, and light. Carbon dioxide levels can be measured, whose concentration is one of the prime contributors to global warming. Installing these around the cities can provide crucial statistical data for air quality that can help assess the air conditions and carry out valuable measures to deal with global warming and bad air quality.

data science and air pollution
The air quality has dropped significantly over the years, and access to clean, fresh air is a hurdle.

Threat to Wildlife

Humankind has been a perilous threat to wildlife. Activities such as hunting, poaching, animal trafficking, and overfishing have devastated the number of species left. Biodiversity and species richness have deteriorated. The current statistics for wildlife degeneration indicate that this matter should be dealt with urgently to save and conserve the wildlife in their natural habitat.

Data science can be a potential source for the conservation of wildlife species of animals. The Nature Conservancy in Massachusetts and the University of Massachusetts Data Science for the Common Good Fellowship Program have claimed that it is possible to construct an algorithm that captures and sorts out trail camera images of animals even if animals’ eyes are only charged at nighttime. 

For a better insight into the movement pattern of several animals, motion-sensitive technology is also employed. Such information is then interpreted by data scientists and used to conserve and restore the natural habitats of these wild animals.

Once the natural habitats of these animals are restored, poachers and hunters can be kept away from the specific area that halts the lingering threats. Furthermore, if any tagged animals go astray or lose track, they can be directed back to their habitat.

Predicting Natural disasters

Approximately 207 natural disasters occurred globally in the first six months of 2020. 95% of the losses and destruction were due to weather-related disasters. These climatic shift-induced disasters cause temporary losses and leave behind a copious amount of destruction that takes several months post-disaster to clean and restore the area. The destruction and devastation are marked with the loss of lives, loss of crops leading to food shortage, lack of availability of clean drinking water, and demolished homes and business setups, all exhausting the economy and the life of the inhabitants.

However, these natural disasters do not always come unannounced. Data science can predict their occurrence, including hurricanes, cyclones, and floods. It uses the data of previous hurricanes; the intensity ranges provide an idea about the prevalence of upcoming disasters and the area it is most likely to hit. All this pre-requisite knowledge enables inhabitants and local governments of the hurricane-prone area to carry out suitable measures.

Data science can predict the occurrence of natural disasters, including hurricanes, cyclones, and floods.
Data science can predict the occurrence of natural disasters, including hurricanes, cyclones, and floods.

Moreover, satellites such as GOES (Geostationary Operational Environmental Satellite) observe the direction of the hurricane current and track it, producing hemisphere images at fixed time intervals. These computer algorithms also detect its occurrence point, called the “eye of the hurricane.” All these data aids in constructing a model that aids in predicting the hurricane pattern and its path. Today, a few of these predictive models include the European Center for Medium-Range Weather Forecast (ECMWF) and National Weather Service’s Global Forecast System (GFS) models.

Floods are a common and catastrophic series that come into action for several reasons: unpredictable rainfall, overflowing rivers, damaged dams, and storm surges. The demolition due to floods can be minimized if sufficient flood forecasting data is available and appropriate actions are carried out ahead of time. Satellite imaging from sources such as the Global Flood Detection System (GFDS) and aerial topography plays a vital role in apprehending the overall flood dynamic. 

Computer algorithms and machine learning techniques can predict the flow rate of water, the temperature and humidity levels near drainage sites, the soil moisture content, real-time rainfall monitoring, and much more. These details provide a better idea about the flood occurrence time, severity, and the specific locations where the probability of occurrence could be highest.

Earthquakes study and observe primarily by seismologists. Though a formidable candidate to be predicted, scientists are finding ways to foresee the origin of seismic waves using machine learning. By utilizing details about seismic signals, their path of travel from source (location) and the magnitude of the earthquake, data scientist strives to unveil ways to predict the earthquakes. Johnson, Los Alamos National Laboratory seismologist, when asked about his view on the use of machine learning to forecast earthquakes, said, “I can’t say we will, but I’m much more hopeful we’re going to make a lot of progress within decades. I’m more hopeful now than I’ve ever been.”

Water Pollution

Pollution in water sources is a matter of grave concern for both marine life and water quality. The discharged effluent (oils, toxic chemicals, plastics, harmful metals) ends up in oceans and rivers that threaten species’ habitats. 

However, machine learning can provide an easier way to clean seas, rivers, and other natural water resources to restore their original conditions. Microsoft utilizes scientists’ access to AI and machine learning technology to protect the environment and save the planet. One such project is the “Ocean Cleanup,” where the focus is on shrinking marine plastic and additional slew issues. Locating and identifying heaps of debris is made easier using AI, which saves time and labor work. This initiative aims to partner the identification system with an automated collection unit to accumulate plastic for its systematic removal.

data science to understand water pollution
Machine learning can provide an easier way to clean seas, rivers, and other natural water resources to restore their original conditions.

Though data science seems to have a bright future being utilized as a critical tool in environmental protection, further advancements and polishing must make it a sustainable practice. The United Nations Development Programme (UNDP) has marked 17 sustainable development goals (SDGs), which should form the basis for using data science to protect the environment and make it sustainable. 

Furthermore, the practical application of data science for environmental protection requires a hefty sum of financial input and resource usage, proving challenging for developing countries. It is preoccupied with other large-scale issues such as combating hunger. 

Moreover, highly educated individuals possessing profound intellect are needed to interpret the AI results to make good use of them accurately. Attaining such a high level of education and mastering the skills required specifically for this domain puts a strain on the finances. Another aspect that needs attention is that although machine learning is automated, it is prone to high error rates. Some errors are kept unfound, and they continue to influence the downstream results of the chain process, thus depriving the data scientist of the accurate picture. Such anomalies can take a significant fraction of time to be detected and then restored.

However, the scope of this technology in different fields of life, especially environmental protection, is paramount. Further developments and advances will enable us to uncover new features that might solve the big obstacle humanity is facing in the modern world.

“The goal is to turn data into information, and information into insight” – Carly Fiorina.

References:

How Data is Changing Medicine

Isna Maqsood
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How Data is Changing Medicine

As computers started taking over the world, they made sure not to miss perhaps the most important field for humans – medical science. Analogue became digital, archives became computerized, and so did medical science.

The major contribution of computer science in the world of medicine has to be data analysis. Data analysis is the process of cleaning, transforming, and analyzing different forms of data. Different kinds of tests can then be run on this reformed data to answer specific questions. Whether the tests are statistical or predictive, they make use of data analysis at every step.

Clinical Trials

Before a new medicine or vaccine is introduced in the market, it needs to be approved by government bodies. This is accomplished by showing them data that supports the purpose of the drug and analysis of said data to show that the results are significant.

People are incentivized to volunteering for clinical trials through attractions such as money. The volunteers are chosen according to certain demographics – age, gender – and their symptoms of the disease that the drug will be used against. These volunteers are then randomly assorted into two groups. One group receives the drug, while the other receives a saline solution or a non-active drug. Participants in neither of the groups are aware of whether they have received the drug or a placebo which ensures the reliability of the data. The results from the study, which constitutes thousands of participants, are added to different software, depending on the type of test that is being run. The difference between the results from both groups is cleaned to remove any extreme outliers and check if the tests’ assumptions are met. When this is completed, the data is ready to be analyzed.

The researcher then runs the tests and compares the results between the two groups. If the drug shows a significant improvement in the conditions of the participants in respect to their illness, it is one step ahead of getting approved and available for mass production and use.

Over 300 million ECGs are done in a year which provides sufficient data to help a machine differentiate between a normal heartbeat and an irregular one.

Diagnostics

Another use of data analysis in the healthcare sector is in the process of diagnosing a disease. People from different ethnicities tend to have different percentages of a disease in their population and show some variation in displaying their illnesses, leading a doctor to make a false diagnosis if they have not previously catered to people of that specific ethnicities. For example, a Hispanic man is most likely to have ‘bad’ cholesterol, and an Asian man is least likely to have high levels of ‘bad cholesterol. Such differences between ethnicities pose risks to the general population for being misdiagnosed.

If data is collected from different ethnicities regarding their portrayal of illnesses and combined, the doctors can use that data to more accurately analyze patients’ symptoms and compare them with the recorded data to make a correct diagnosis. 

Data analysis also contributes to diagnostics with Artificial Intelligence (AI) to compare new data with previous ones, like observing irregular heartbeats. Over 300 million ECGs are done in a year, providing sufficient data to help a machine differentiate between a normal heartbeat and an irregular one. Similarly, Stanford researchers have developed an AI software that observes skin tags and lesions and accurately diagnoses them as benign or malignant tumors.

Medicine and data
Another use of data analysis in diagnostics is Artificial Intelligence (AI) to compare new data with previous ones.

These methods of diagnosis with data analysis are fast, effective, and accurate. They can potentially save millions in finance as they prevent lengthy measures such as blood tests and biopsies. An initially high investment can lead to long-term savings that can be used in different healthcare departments.

Predicting Outbreaks

Different pathogens surround us. These disease-causing organisms are transferable and pose a big threat to healthcare systems. Recently, we have encountered the Coronavirus, a pathogen that spreads between people who come in close contact with someone already possessing it. The outbreak of this virus originated in China and has managed to spread all over the world.

Researchers collect data about the probability of the pathogen being in an area, the rate of transference, the likelihood of having the disease, the rate of travel within countries and between countries to examine how big of an outbreak there might be, and the causes for it. Areas within countries that are more likely to have a larger outbreak can better prepare their hospitals and staff to deal with it. Similarly, areas or countries with low rates of outbreaks can take precautionary measures to prevent a wider spread, such as imposing travel bans.

Staff Requirement

Another unusual use of data analysis in healthcare is the prediction of staffing. Using previous admission data of patients at different times of the year and the day, data analysis can predict how many patients are expected to make use of a hospital at any given time. Hospitals are crowded during the different holidays or at certain times of the day, which can cause an understaffed hospital to run slowly and lead to mistakes, some of which might prove fatal to a patient. If data analysis is used to predict the number of patients coming in, hospitals can have an adequate staff present. This will help prepare the hospital to have enough staff available on-site so that the hospital’s work can move more smoothly. By doing so, hospitals would avoid being understaffed and prevent the essential workers from being overworked to the point of exhaustion, which is a major cause of error in hospitals.

Feedback

Feedback is an important source of gaining information on how to improve service. Hospitals and clinics provide an essential service to people; hence they need to know anything that can help improve it. Feedback can be gained by having outgoing patients or their families fill a form or calling past patients and asking them a few questions regarding the service. These questions can include the response timing of clinicians and nurses, the behavior of the staff, the waiting time, and even the food from the cafeteria. Patients can be asked about their opinions on improving the service they were provided and how likely they were to recommend that service to their fellows. Their answers can then be input in software and analyzed to observe the major responses people have the most targeted recommendation that can then be implemented in hospitals.

medicine
Feedback can be gained by having outgoing patients or their families fill a form or calling past patients and asking them a few questions regarding the service.

Predicting Service Requirements

At times, countries have either too many hospitals or too few hospitals to provide for their population. In too many hospitals, some hospitals might be of easy access to people while others might not be, leading to a discrepancy in the ratio of doctors to patients. When there are fewer hospitals than an area requires, the present hospitals are overcrowded, and service to patients becomes slower, leading to more errors and overworked staff. If the number of patients using hospitals in a certain area is carefully analyzed, it can help predict how many hospitals are required in that certain and the size of these hospitals.

If an area has a larger number of hospitals than it needs, then some of these hospitals can be shut down, saving finance that goes into operating them. This saved money can be input into existing, working hospitals to improve their efficiency and patient satisfaction.

On the other hand, if an area requires more hospitals, the government and the private sector could be made aware of it. This would prove to be a great investment for both sectors and would benefit not only them but also the wider population.

Preventing unnecessary Emergency Room visits

Normally, data between hospitals is not shared. This means that if a person were to go to the Emergency room (ER) in two different hospitals, at two different times, for the same issue, they would have to get all the tests and checkups done twice. This can be prevented if the data of patients is shared between ERs. The patient’s details could be cross-referenced to patients from other ERs, and if they happen to have been checked at another hospital for the same complaint previously, the next doctor could be made aware of this. They could have access to their test reports and medication prescribed so that the treatment they propose next is built up from the previous one and not started from scratch. This method helps prevent unnecessary use of lab facilities which are a wastage of precious time and money. People who use the method of going to different ERs to get prescription drugs such as pain killers or opioids to abuse can be distinguished and help save resources while also lowering drug abuse rates.

Conclusion

Data analytics may be a part of data science, but it is hard to ignore its importance in the medical world. Not only does it help researchers to finish their studies, but also the general population and the hospital workers. It helps allocate funds to the right departments, prevents wastage, and is time-saving for organizations and healthcare workers. If we were to use data analysis fully, we would get the listed benefits and many more. And who knows, we may even be able to cure cancer with it!

References

Also Read: TALKING CLIMATE CHANGE, DISASTER MANAGEMENT, AND THE GEOLOGICAL STATE OF PAKISTAN WITH DR. QASIM JAN

Blockchain: The crucial technology behind CryptoCurrency

Shoaib Shahid
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Blockchain: The crucial technology behind CryptoCurrency

“Bitcoin soars, “Cryptocurrency Ethereum on the Rise,” “Bitcoin rises 9.8%..” It has become nearly impossible to avoid such headlines. I am sure that the person reading this is already aware of the ‘craze.’ But you may not be aware of where it came from, who is running it, and, most importantly, what is it? Throughout the article, we shall talk about bitcoin as it is the most renowned cryptocurrency. But the workings we’ll apply to all types with a few caveats here and there.

Bitcoin is the first of its kind. It is an intangible and decentralized currency that is entirely digital. The preceding definition essentially consists of two major parts: decentralized and digital hence ethereal. A Lot of the intrigue surrounding bitcoin comes from the decentralized part, which means that there is no central authority (e.g., the Bank) regulating anything. To fully understand it, we should take a look at our dealings with ‘normal’ money.

Let’s say that you want to send some money to your friend. You pick up your phone, open up your Bank’s website and send 5000 Rupees to her account. Soon after, you will probably receive a thank-you message from your friend. What has happened is that the specified number has been deducted from your balance at your Bank. The opposite has happened at her end; 5000 is added to her balance at whatever Bank she is affiliated with. It is understood that the Bank is of crucial importance here. But what if you use cryptocurrency instead? On the front end, everything will seem quite familiar. You pick up your phone, open up your bitcoin wallet and send crypto coins or cryptocurrency or any other coin to your friend. Hidden behind all the technology, the transaction itself happens, and this is where things drastically change. Crypto coins or cryptocurrency are deducted from your wallet and added to her Wallet. In the absence of a central authority, who does this? You and your friend yourselves! And in another case, if your coins are on the exchange, then the exchange will work as a bank, and they also have their transaction fees during this process.

THE FOUNDATION

If there is no central authority that has a ledger storing all transactions and balances, there has to be an alternative to avoid fraud and the risk of loss. If there is no such feature, anyone could say that their friend gave them while their friend is unaware of this. A method to prevent this is what provides a currency its value. 

If there is no central authority that has a ledger storing all transactions and balances, there has to be an alternative to avoid fraud and the risk of loss.
If there is no central authority that has a ledger storing all transactions and balances, there has to be an alternative to avoid fraud and the risk of loss.

Without a central authority, how are you to trust anyone’s claims regarding their balance? The answer is simple. You don’t have to. The need for trust is entirely sidestepped by providing every bitcoin owner with an independent record of all past transactions that have ever happened. A user cannot add or edit the information unless authorized by all other users. This is called ‘blockchain technology,’ which is what all cryptocurrencies are based on.

When a transaction goes through, the computer sends out a signal containing information about the transaction to every bitcoin wallet. This also happened when you sent the cryptocurrency to your friend. Your phone sent a signal containing this exact information into the bitcoin network. Every transaction made is eventually added to a block which is then added to the blockchain. We’ll discuss this in more detail later.

CRYPTOCURRENCY

Due to the nature of the information being sent, its security is critical. To ensure as such, one can digitally sign their message. Digital signatures, similar to handwritten signatures, offer proof that you approve of what has been signed. A digital signature relies on asymmetric encryption. This is a method to encrypt or decrypt your data using a pair of keys. The private key is only known by the owner, while the public key is available to everyone.

The outcome is what is known as a digital signature. Without your private key, a third ‘friend’ cannot just send out the message that you paid him 10 bitcoins, thus officially reducing bitcoins from your wallet and adding them to his. To further intensify the security, even if the message is slightly altered, the digital signature completely changes. This prevents anyone from copying your signature and adding it to another letter. If anyone wants to verify a message, they can apply the owner’s public key to tell whether its matching private key produced the digital signature.

cryptocurrency
An idea central to bitcoin and its functionality is a cryptographic hash function.

But digital signatures are not the only type of cryptography used. An idea central to bitcoin and its functionality is a cryptographic hash function. A hash function is basically a mathematical function that converts an input message into a collection of bits. This is done according to a set of rules that make up the function. More specifically, a cryptographic hash function creates an output that is nearly impossible to reverse engineer into the original message.

DATA MINING

We are still to address the significant problem that may arise in all of this. How can we trust that the message we received has been received by everyone else? Or that the block of transactions we have does not conflict with other blocks? This is where data miners step in and earn their due.

When you make a bitcoin transaction, you pay a small fee, usually a reward for the miner to validate your transaction. When a block is filled with transactions, it is verified by millions of computers owned by miners on the network. Miners are the people with computers that add transactions to a new block. But to do so, they have to solve an extremely complex mathematical problem. A miner aims to find a number to add to a block, leading to the hash address of that block. In a blockchain, a block contains the hash of the previous block, the transaction details, a 32-bit number called a nonce (the number the miner has to figure out), and the hash of the block itself. Due to this, solving the problem gets increasingly difficult as the blockchain grows. 

This process needs a lot of computational power as computers usually guess the number rather than reaching it by solving the problem. When a lucky miner guesses the correct number, their block is added to the chain, gaining some bitcoins. This ingenious solution of data mining or ‘proof of work’ was suggested in the original paper of 2008 that initiated bitcoin. Many cryptocurrencies use another method known as ‘proof of stake,’ which doesn’t require any mining, saving energy, and computational power. Similar to this change, new cryptocurrencies are innovating and integrating consistently, whether they’re catering to specific niches or being more general-purpose. Whatever opinion anyone holds of this phenomenon, there is no denying that we are all excited and curious to see where this leads us economically and technologically.

Bibliography:

https://builtin.com/blockchain

https://blockgeeks.com/guides/what-is-blockchain-technology/

Talking Data in Healthcare and Opportunities for Women with Dr. Bushra Anjum

Saadeqa Khan
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Talking Data in Healthcare and Opportunities for Women with Dr. Bushra Anjum

The healthcare industry is one of the most striking beneficiaries of data sciences. In post-Covid world medical diagnostics, medical treatment is becoming more efficient and accessible, medical treatment more personalized, and medical research more data-driven.

Data scientists drive innovation across the healthcare sector; like chatbots that can help patients find a good physician, productivity applications that can automate administrative tasks, and recommendation services that can identify patients who could benefit from a new clinical trial.

Dr. Bushra Anjum is among the few inspiring ladies leading in data sciences and is currently based in California. Born and raised in a middle-class family in Pakistan, her father served in the Pakistan Army, and she’s the youngest of three daughters. While doing her master’s in computer science from LUMS, she showed interest in studying abroad for a Ph.D. degree. Fifteen years ago, she came to the US, and with her hard work and devotion to her career, she’s now a trained data scientist, having worked extensively with predictive analytics. Currently, she is working as the Senior Analytics Manager at Doximity, a San Francisco-based health tech company.

Below is the recent conversation we had with her. Dr. Anjum discusses the promise of data in the field of healthcare, some fascinating details about the data products she has been building, her volunteer ventures, and how women are essential to create an equitable data-driven future for all of us.

Let us know about your life and career? Who inspires you the most for an offbeat career like a health data scientist?

Dr. Bushra: I am a trained data scientist, having worked extensively with predictive analytics, and currently working as the Senior Analytics Manager at Doximity, a San Francisco-based health tech company. I received my Ph.D. in Computer Science at North Carolina State University in 2012, then served in academia (both in Pakistan and the USA) for a few years before joining the tech industry. I joined Amazon and worked for the Prime team, where I was a backend engineer for four years. My research background is in performance evaluation and queuing theory. Combining that with the engineering expertise gained during my tenure at Amazon, I switched to the field of data science, which brought me to Doximity. I joined as a data scientist and later got promoted to leading the revenue wing of the company.

The inspiration for my work is the patients and their caregivers, family, and friends who are not hired to do the job but do it out of love and concern. Our health care systems worldwide need to be effective, yes, but equally if not more important, they need to be empathetic. No one needs software complexity, vague instructions, incorrect diagnostics, and unsafe personal and financial data on any given day. However, consider dealing with all this on top of the emotional burden, worry, and life & death uncertainty that patients and their caregivers fight through; it almost becomes criminal. Modern day data analytics and data tools have the potential to make the patients’ and caregivers’ life easier, and I would like to play my part in making it happen.

Can you share an experience when you gathered data from multiple resources and combined it into actionable insights for your company? How did you determine which source was relevant and how good your product is performing? 

Dr. Bushra: I am happy to share some details of a data product called “Press Boost”, that I designed and implemented. But before going into the product detail, some background on the company, Doximity, is needed. A comparable sometimes used to introduce Doximity to an unfamiliar audience is like LinkedIn for doctors. Only verified physicians can join the network, making the conversations, discussions, and referrals safe and HIPAA laws compliant. Doximity aims to be the newsfeed of medicine, personalized for each verified physician. We have the largest readership in medicine, have an in-house editorial team, and analyze (and surface) 200K+ articles per week. “Press Boost” is a (free) data product that helps source articles with high medical value and engagement potential for our clients. 

We ingest thousands of medically relevant articles daily from hundreds of online news publications (for non-journal organic articles) and PubMed (for research-based journal articles). After ingestion, the articles go through various rounds of NLP and regex matching to determine their medical relevance and extract hospital and medical facilities mentioned in those articles. Successful completion of these steps gives us a mapping of how medically relevant an article is and which hospital systems and facilities it mentions.

The product that I built, “Press Boost,” helps source articles with high engagement potential. First, it tracks internal trending articles based on engagement on Doximity (clicks, likes, comments). It then also looks at external trending research articles by ingesting the Atlmetric score (how much and what type of attention a research output has received) and the Mendeley readers score (how researchers engage with research on Mendeley). Finally, all of these factors are weighted and combined into scores. Such top-performing news content is then redistributed to clients associated with or interested in the hospital systems mentioned in those articles.

For interested readers, there is a beginner-friendly “Product Spotlight: Press Boost” presentation available on our website.

Being a leading woman in health IT data science, how do you overcome career obstacles? Have you encountered gender discrimination or sexism?

Dr. Bushra: I face the same challenge that every other woman in the technology world faces, the dichotomy of dual expectations. This is best explained by Dr. Deborah Gruenfeld, a social psychologist and professor at Stanford Business School. She defined the dual expectations as playing high, which means you show your authority, power, influence, and playing low, which means you are more approachable and likable. (See her video explaining the concept here). As leaders in the technology field, we are expected to play high, but as women, we are traditionally expected to play low. So, when we play high, we are deemed not likable, and when we play low, we are considered to be not competent! It’s a continuous balancing act. Patience, good judgment, and wisely picking my battles have been my friends in this journey.  

CRA-W MS vs PhD Advice Session Data
Dr. Bushra Anjum speaking at a Computing Research Association (CRA) Advice session. Credit: www.bushraanjum.info

As far as gender discrimination and sexism is concerned, there is no denying that it exists. I, however, have a certain approach to dealing with it. I read this great quote by Deepak Chopra “What you pay attention to grows. If your attention is attracted to negative situations and emotions, then they will grow in your awareness.” Hence, I don’t actively scan for discriminatory behavior, as that will put you too much into the fight or flight zone (thanks amygdala!); however, if discrimination openly finds me, I fight against it with all my strength, courage, and prudence.

Being associated with ACM (as a senior editor) and ACM Women (as the standing committee’s chair), what are some of the contributions you are most proud of? 

Dr. Bushra: I am a keen enthusiast of promoting diversity in the STEM fields, especially encouraging women to be a part of the evolving disciplines of Computer Science and Data. I am a volunteer at Association for Computing Machinery – Women, Computing Research Association- Widening Participation, Rewriting the Code, TechGirlz, MentorNet, to name a few, and some regional groups like Pakistani Women in Computing and WomenInTechPK. Some of the most rewarding experiences in my life have been as a volunteer.

I am a senior editor for Ubiquity, ACM’s peer-reviewed web-based magazine devoted to the future of computing and the people who are creating it. I have been the first female member of our editorial board and started a new section, “Ubiquity: Innovation Leaders,” which consists of interviews with young professionals who comment on their concerns about the future of computing and their ambitions to shape the future through their leadership. As a result, I have been able to present several moving and compelling stories from diverse backgrounds and computing disciplines. 

I am also the Standing Committee’s Chair for ACM-W. There I got the opportunity to propose a new initiative, a web series, “ACM-W: Celebrating Technology Leaders.” The idea is to bring stories and advice from engaging speakers, women with diverse careers in computing, directly to our global audience.

Pakistan has a severe lack of peer-reviewed and general science magazines. What role do you think magazines like Scientia Pakistan can play in promoting science writing culture in Pakistan, especially among university students?

Dr. Bushra: I genuinely believe that Scientia, with its mission to re-shape the narrative of science journalism in Pakistan, is doing an excellent service not only for the Pakistani student community but for a global audience. 

Science is not an elitist club’s game, and good writing is not an outdated skill. Scientia is working towards mitigating both these misconceptions. Science is ubiquitous, everywhere working for everyone; hence it should be accessible to everyone. One’s writing reflects one’s personality, and “unedited incoherent streams of consciousness riddled with cyberslang, shorthand, and emojis” is not an attractive personality type. Scientia enables, encourages, and brings quality scientific writing to the masses without compromising either accessibility or scientific merit. Kudos to the entire team!

I believe active partnerships with leading universities and software houses in the country, encouraging academicians and practitioners to contribute regularly, will increase the visibility and impact of the initiative.

How do you see the field of health data evolving?

Dr. Bushra: Data Science, combined with machine learning & artificial intelligence advances, has enormous potential for improving the health industry. Data science can improve the speed and accuracy of testing and diagnosis, improve health research and drug development, strengthen diverse public health interventions, etc., and never has the utility been more apparent than in the COVID-19 era. e.g., in the last year and a half, data has been extensively used to

  • Understand and predict the pandemic spread (using principles from network science, econometrics, applied microeconomics, etc.)
  • Create effective treatments, creating algorithms capable of computationally generating, screening, and optimizing hundreds of millions of therapeutic antibodies (gene sequencing, computational biology, etc.)
  • Resume and maintain economic activities (epidemiological modeling, epidemic dynamics, social networks, time series analysis, agent-based, network simulation, complex systems, etc.)
  • Track spatial distribution of COVID-19 (contact tracking, geo-visualization, spatial data science, digital biomarking, remote sensing, etc.)
  • Understand the evolution of hate speech, misinformation (large-scale measurements, social media, game theory, etc.)

However, the value proposed can only be realized if ethics, empathy, and civil liberties are at the core of the algorithmic design, data modeling, deployment and analytics usage. Two of the most significant issues in the data world are (1) unethical collection and use of patient data and (2) biased algorithms. World Health Organization (WHO) has recently released WHO: Ethics and Governance of Artificial Intelligence for Health Report (June 28th, 2021) that identifies six principles to ensure AI works to the public benefit of all countries. These principles are protecting human autonomy, providing informed consent, quality control, and transparency of the algorithms, inclusiveness irrespective of age, gender, ethnicity, etc., and transparent continued monitoring during actual use. I firmly believe these are indeed the six areas of future growth, research, and practice in the field of health data. 

Do you think that there are significant opportunities for women data scientists in healthcare management?

Dr. Bushra: As I mentioned before, data science faces two significant challenges: unethical collection and usage of data and biased information. A major source of bias in many datasets is that the people who collect, organize and analyze the data do not represent the people that will actually be using the technology. For example, according to Harnham’s Diversity Report for the US Data & Analytics industry 2020-2021, women hold only 18% of the data science jobs, and the problem is likely worse in most lower-income countries. Data, in most cases, is like Rorschach charts; people see their own values, interests, and experiences reflected in them. If not careful, this opens the door to bias at every stage of the data value chain (sourced from Open Data Watch).

Data, in most cases, is like Rorschach charts; people see their own values, interests, and experiences reflected in them. If not careful, this opens the door to bias at every stage of the data value chain
Data, in most cases, is like Rorschach charts; people see their own values, interests, and experiences reflected in them. If not careful, this opens the door to bias at every stage of the data value chain

The underrepresentation of women, or any demographics, in data science increases the possibility that the data-driven decisions and products will not represent their interests or, in extreme cases, may harm their interests. I would highly recommend reading Carolina Criado Perez’s award-winning book “Invisible Women: Exposing Data Bias in a World Designed for Men” which talks about the issue in detail. One of the best ways to mitigate bias is to make sure that the data team consists of diverse experiences and perspectives to begin with. There is a global business realization that interpreting causal relationships and correlations in large data sets requires subtlety, and women bring different intuition to the table. The field of data science is exploding with opportunity. So yes, not only do women have a lot of scope in the field of data science, but this may be one of the best times to enter the field fueled by COVID-19.

What advice do you have for future data scientists, especially women? 

Dr. Bushra: My advice is a little broad that any young man or woman in the STEM field may be able to gain benefit from, should they agree with my point of view of course.

We are at the brink of the fourth industrial revolution, powered by a fusion of technologies that are quickly blurring the lines between real and virtual, physical and digital. We need to guide and inspire a tech workforce ready for this unprecedented, disruptive future where quick obsolescence may be the biggest threat and remaining relevant, the biggest struggle. The most important training in this regard is to help future STEM professionals grow a generalist mindset. Rather than being tied to or specialized in a particular language, framework, or solution, generalists have a basic working knowledge of multiple domains, principles, and technologies. This helps them remain relevant in a variety of engineering jobs and projects. Moreover, they know “how to learn” and thus can quickly come up to speed and morph as per given technical preferences and constraints. 

Second, this I share, especially for the women readership, you don’t have to negate parts of your personality to be perceived competent. I have shared this advice before, but I believe it cannot be reiterated enough times. For example, have you heard (admittedly well-meaning) statements like, “sure, humility is a good value, BUT it’s time to set it aside and work on self-branding”? It doesn’t have to be one or the other. You can be humble AND work on self-branding. You can be polite, flexible, and accommodating AND not let people take advantage of you. You can use “sorry” in your conversation and “just” in your emails if it is part of your politeness language, AND make sure you are being taken seriously. Whatever comes naturally to you, whatever feels authentic, is ok to hold on to while still evolving to a better self. If you want to change and leave some personality traits behind, that is fine, too, as long as you don’t feel obligated to do so.

Dr. Bushra can be reached out on Twitter @DrBushraAnjum or via her website https://www.bushraanjum.info/

Also Read: EVERY DATA HAS A STORY: VISUALIZING AN IDEA BEYOND DATA

Every Data has a Story: Visualizing an Idea beyond Data

Sandeep Poddar
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Every Data has a Story: Visualizing an Idea beyond Data

“Data is the new gold.” We all are familiar with this phrase. Data can change how corporate giants do businesses, sports professionals plan their strategies and even win you elections. Data is a reliable form to present and prove hypotheses and abstract ideas to tag them as universally acceptable information. Data itself is a strong medium to prove almost anything concerning everyday world affairs. But to make the most of the data, we need to extract ideas, showcase them pleasingly, and use them to create an impact story. This forms the realm of data communication. 

THE VISUAL STORY

Visualizing the data is a part of serving it in a beautiful, comprehensive, and compelling form that is a visual treat. Scientifically, we are more likely to remember the visual aspect of the information than in another form. Also, data needs to be represented in a way so that the end-user can extract the idea or the message from it based on proper observations. Data storytelling or communication is the ability to convey the idea to a large (and targeted) audience with a simple and clear message. The story behind the data must have a context. This broadly refers to why is the story is being told and for whom it’s mean to? 

The need for a story arises when we want to showcase an idea hidden behind a data set; a data set is obtained from discrete or continuous record-keeping of an event or activity. We analyze the data, distill it until we observe a pattern, find the reason behind such pattern, and conclude. This can help us make decisions, maximize profit and minimize losses. For example, the following dashboard maintained by the World Health Organization keeps us informed about the global spread of COVID 19 cases in real-time. The darker areas in this visual are the most affected, while the lighter colored are relatively less affected. This visualization tells us the story behind how massive this pandemic has become. Similarly, giving a visual form can help you tell your story about a given data set.

WHO Coronavirus database
Image source: https://covid19.who.int

TYPES OF DATA VISUALIZATIONS

Data visualization can be of two types, exploratory and explanatory. Exploratory visuals can help us find the fundamental reasons and Logics that lie beyond the data. Some basic questions about the data can be solved using exploratory visuals, like what? Why? These visual representations need contextual hypotheses or presumptions about a possible outcome. For example, how is the literacy rate of a state-related to its standard of living? Why do mineral-rich countries are some of the poorest in the world? Explanatory visuals, also called infirmity visuals, are used when a specific aspect of already established data is to be communicated. If the data is communicated in a comprehensive and empowering way, the audience gets to draw insides, identify correlations, recognize trends, and ultimately form their own fact-based story.

The need for a story arises when we want to showcase an idea hidden behind a data set; a data set is obtained from discrete or continuous record-keeping of an event or activity.

DATA VISUALIZATION TOOLS

Data visualization tools provide an easier way to create visual representations of large data sets. These data visualizations can be used for various purposes: dashboards, annual reports, sales and marketing materials, investor slide decks, and virtually anywhere else information needs to be interpreted immediately. This ability of data interpretation is called Business Intelligence. An enterprise could use data visualization to discover what areas in the organization are pulling it back. This could be in the business sector; business intelligence lets the enterprise identify some problem areas in their business. 

A data visualization tool makes work easier, especially if you’re dealing with big data. Even then, there are numerous Data Visualization Tools in the market today.  A good data visualization tool has the following features: First is the ease of use. There are several complex tools available for representing data in a visually appealing way. Some have excellent tutorials designed in ways that feel intuitive to the user. In contrast, others lack this convenience, thus eliminating them from any list of “best” tools, regardless of their other capabilities. A good tool can also handle huge sets of data. In fact, the very best can even handle multiple sets of data in a single visualization. It also can output an array of the different charts, graphs, and map types. Most of the tools can output both images and interactive graphs. There are exceptions to the variety of output criteria, though. Some data visualization tools focus on a specific type of chart or map and do it very well. Those tools also have a place among the “best” tools out there. Finally, there are cost considerations. While a higher price tag doesn’t necessarily disqualify a tool, the higher price tag must be justified in terms of better support, better features, and better overall value.

There are mostly 3 types of data visualization software; open-source, free data, and proprietary data software. The software version is fully paid for and is accessible via cloud or installed in the standalone client-server with proprietary. 

Microsoft Power BI is one of the popular data visualizations tools. Its popularity can be attributed to its ease of use. It offers visual-based discovery, augmented analytics, data preparation, and interactive dashboards. It offers various data visualization capabilities and features such as visualization through natural languages and custom visualization. It offers access to cloud-based and on-premise data sources like Google charts or Google analytics. However, all these features come at the cost of an annual subscription from Microsoft. Nevertheless, it has got a reasonable justification for the price it makes you pay for it.

But, if you still don’t want to spend a penny on BI tools, go with Google Data Studio. Data Studio is one of Google’s Data Visualizations Tools that are designed with ease of use. It is free and open-source that lets you integrate data set to the Google ecosystem. It’s designed for enterprises that wish to integrate their Google data quickly. It may not fit businesses that need a high-functioning Data visualization tool as it may lack formatting and visualization. It turns information into informative, fully customized dashboards and reports that are easy to read, present and share. The best feature it offers is the degree of integrations it offers to create a data source. You can even use your YouTube channel data to create a dataset. If you are thinking of learning the basics of data visualization, try your hands-on Data Studio. Below is a visualization on Google Data Studio showing the difference in the research expenditure of countries over a period of time.

How do countries can spend in research and development
Image Source: https://datastudio.google.com/gallery

Tableau is another free Data Visualization Tools Open Source that allows creators to get used to the tool with little investment easily. It creates a platform for sharing data visualization and insights. Tableau Public is designed for customers who need to evaluate Server and Desktop applications. A tableau is a good tool for businesses that do not need vast features and want software that is easy to use and affordable. On the downside, the information and data used in the tableau desktop tool are public and can be accessed by anyone; hence they aren’t secured. Tableau comes as an open-source with a free plan.

Choosing the right and best Data Visualization Tools can be tricky, but it’s rather easy. Start by analyzing the features they offer, such as language support and cross-browser testing.  Regardless of whichever tool you pick, you should also ensure that the tool meets your needs.

WHY TELL A STORY?

The art of data visualization might involve using shapes, geometric colors, graphs, and other things to represent your data visually. All you need is to create an interface that can be interactive. Now, you have the responsibility to showcase the data in the best possible way, and it’s up to the audience (or the end-user) to make the most out of your data. When it comes to storytelling, the responsibility is on your shoulders. Now you’re responsible for what the user gets from your representation of data. It’s much like the art of communicating expressions. Whatever you deliver must ensure that the audience must have a take away from it; that’s your responsibility. You have to prepare beforehand and think of the outcome before you start creating a story to tell. So if you were telling it, it’s your responsibility that people get it.

Almost everything can be represented in the form of data. And every data can be expressed in visuals. Data, when represented in a visually suitable way, always has a story to tell. And, a story based on authentic sources is the best way to make an impact and, largely, a change. Happy Communicating!

References:

Also, Read: Unique Story of a Cyber Crime

The Role of Forensic Science in Wildlife Crime Investigation

Aniqa Mazhar
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The Role of Forensic Science in Wildlife Crime Investigation

One cannot imagine the level of deviance human beings have figured to get themselves knee-deep in. Animals are part of this planet and have their own ecosystem and world in which they are immersed. Interfering in their little world and then disrupting it for them when they’ve wronged us in no way is surely a crime.

Some examples of prevailing wildlife crimes include trading and smuggling, for example, poaching elephants for ivory. Other crimes include illegal trafficking, obtaining and consuming wildlife flora and fauna e.g., timber and other forest products, against the law. Illegal hunting and fishing are also high on the list. According to wildlife crime reports, the pangolin is the most poached animal in the world.

When I heard the name of data science, the first thing that popped up in my mind was a long tabular display of numbers and statistical formulae to play around with them. I had little to no idea that this emerging science field could help investigate crimes against wildlife. Let’s dive deep into data analysis of the recent rise in wildlife crimes.

Investigating wildlife crimes involves analysis of DNA- deoxyribonucleic acid. So, we’re talking about wildlife forensics here, basically.

Wildlife forensics

Forensic science is also known as criminalistics and is applying scientific techniques to identify criminals in investigations. Wildlife forensics involves tracking down the animals mysteriously disappearing or being smuggled etc.

Some of the common techniques used in wildlife forensics involve microscopy, PCR, and DNA sequencing. Since wildlife crimes have become global and organized threats these days, the techniques used are also technologically advanced. Talking about microscopy, samples found at crime scenes can be analyzed in detail using the different kinds of microscopic techniques now available.

Wildlife Crime Program | IFAW
Some examples of prevailing wildlife crimes include trading and smuggling, for example, poaching elephants for ivory. Credit: IFAW

We can use microscopy when dealing with crimes related to mammals because analyzing hair samples is always part of the crime scene. We can analyze the type of hair in question, and from that, we can figure out which animal the hair belongs to. For this, we need a data bank that already has the analyzed data from the hair of common mammals. Different parts of the mammal hair can be compared, like the general hair profile, the root and shaft of the hair, the outer cuticle of mammalian hair, the solid cortex or center of the hair, the inner core of the hair, or simply the cross-section of the whole hair cell. An advanced technique like scanning electron microscopy is used to capture pictures from different angles, and this pictorial data is then analyzed.

In cases where mammals are not involved, DNA analysis remains the king of all techniques. This is because DNA can be extracted from any kind of body cell and analyzed. The basic protocol followed is extracting DNA from the cell, increasing it in number by the Polymerase Chain Reaction, and then sequencing it to reveal the exact genetic code.

In forensic genetic identification, this DNA data is used first to identify animal species. This is done by analyzing the sequence similarity of genes with a certain species’ genes in the databank. Secondly, it is used to identify the geographic origin of the species. Once we know the species, we can track down where it is common. This is a key lead in our forensic case as we are now knowledgeable about the location of our wildlife crime.

Another technique used in wildlife forensics is the study of elemental profiles. This means comparing the different types of isotopes present in our sample. This is especially helpful when we need to find out if our animal was from the wild or bred in captivity. You see, the sources of food consumed in both these habitats may have different isotopes of organic elements, so elemental profiling becomes necessary.

The main method used to study isotopes is mass spectroscopy, which uses the principle of comparing the masses of different molecular compounds with relation to the possibilities of all the isotopes of the atoms present in them.

While these techniques all require special apparatus and ski much simpler ones used along with the above ones, we all know how important footprint analysis is. This takes me back to my childhood where I loved the cartoons Scooby-Doo, the dog, along with his group of human friends, was always solving ghost mysteries with a magnifying glass glued to one eye in search of clues.

Forensics to support the fight against wildlife crime | CITES
In forensic genetic identification, this DNA data is used first to identify the animal’s species. This is done by analyzing the sequence similarity of genes with a certain species’ genes in the databank. Credit: CITES.org

Footprint impressions can help us track down the path of a species and identify the species as well as their age and size. But since there are so many animals in the wild, footprints of different species are often mixed up in the ground or soil, which may pose a challenge for the tracking-down team of scientists.

Serological techniques involve interaction between the sample cells and species-specific antibodies to confirm the identity of the organism the cells belong to. This is a precise and thus accurate detector, but most labs do not have antibodies for the serum of many species.

Recently, three new techniques applying the principles of radiation have been implied to the field of wildlife forensics. Firstly, we have infra-red techniques, which are basically very good at detecting organic compounds in body fluids and samples and several kinds of fibers like hair, fur, etc. This technique is advantageous over the rest because it is cheaper and highly reliable as it requires easy examination of samples like soil, food, etc.

An example of its application includes identifying the geographical roots of herbal medicines.

Another hurdle faced by wildlife investigators is the presence of metal ions in their samples. For that, a technique called Inductively coupled Plasma Atomic Emission Spectroscopy is used. The ions present in bone or other tissue samples link us to whether the animal experienced a kind of natural or man-made catastrophe like a fire or bomb blast.

Radioisotope tracer techniques are used in wildlife forensic cases as well, where carcasses, teeth, talons, tusks, feathers, or stomach contents can be analyzed to see if the animal has been exposed to any kind of radioactivity.

Although most of these techniques may sound fascinating, they are not simple to perform. Sometimes several techniques need to be done collectively, and long data analysis is required before answers can be found.

Some Limitations of Forensic Science

The biggest hurdle is not marking the crime scene and finding the evidence but, in fact, preserving and recovering the evidence. Footprints can easily be eroded or erased. Hair, teeth, claws, etc., start to decay, and decomposers start to feed on them. Species identification is only possible if there is information beforehand in the databases. Most biochemical techniques require skilled labor, maintaining machinery, and a high cost. Since wildlife forensics is not much of a focused career in developing countries where the law is still fighting for human rights, many of these techniques are still to be made available. Modern laboratories, training for forensic scientists, and collaboration with international wildlife organizations are all steps that could lead to a better future for wildlife forensics.

References:

Also, Read: Trafficking of Pangolins is putting them at risk

Deepfakes Explosion— Impact Unraveled

Saadeqa Khan
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Deepfakes Explosion— Impact Unraveled

The term “Deepfake” was first coined when a Reddit user with the same name shared a few digitally engineered adult videos of a couple of female celebrities in 2017; he used deep learning technique for the alternation.

Deepfake, one of the novel forms of misinformation, has become a real challenge in the modern communication environment due to its rapid spread through online news and social media. 

Falsehood, fantasy, fake news all have walked along with the development of modern communication and journalism. There is evidence of misinformation since the Roman Empire (Barkhart, 2017). It was later developed as a strategy with the invention of print in the 15th century. The possibility of disseminating written misguided information in a faster way made the circulation of fake news much more accessible. 

One of the remarkable examples of misinformation of our time was the radio broadcast of the “War of the Worlds” directed by Oscar Welles on Oct 30, 1928. This radio show was followed by thousands of listeners, and a minority believed that our planet is under the aliens’ attack, thanks to the fabulous narration of Welles. 

The misinformation has been using against enemies in conflicts and wars, as we witnessed in World War I & II, Vietnam War, and the Gulf crisis. A recent example of fake news propaganda was of China and the US against each other. After the emergence of Coronavirus from Wuhan, the irresponsible statements/ tweets of former US President Donald Trump added fuel to the fire. 

There have been different proposals for classifying fake news during the last few years, like news satire, a prevalent form of fake news with a significant presence in magazines, websites, and radio or TV shows. And news parody, which shares some of the characteristics of news satire, but is not based on topical issues. 

The promoters of these pieces mostly try to deceive by blending them among the truthful ones. Like the spread of Coronavirus first linked to 5G network and then claimed that it was engineered by Bill Gates. It was told to the public that they can detect the virus-germ by placing a halved onion in their rooms and vice versa. The wave of misinformation and myths was so intense that WHO introduced a new term, “infodemics”. 

Danielle Citron, a Prof of law at the Boston University, says, “it appears that the deepfake technology is being weaponized against women mostly for the revenge.”

The fake news conspiracy theories/ stories trend is not new; they have always been there, but in recent years, corresponding to the rise of social media, the public’s interest in fake news has grown sharply because “Controversy sells”. 

Presently, one out of five internet users gets their information via YouTube, Facebook, or Twitter. We live in a post-truth era characterized by the digital information warfare running by the media giants to manipulate public opinion for their personal interests. 

Besides, photo manipulation, alteration of images, and more recently, videos are widely used to build a different reality for advertising and public relations. In Nov 2019, Hao Li, pioneer of deepfake, stated that “this trend is growing more rapidly as I was expected. Soon it’s going to get to the point where there is no way that we could actually detect deepfakes anymore, so we have to look to new technologies for the solution.”

Hao’s AI firm Deepfake found over 15,000 fake videos online in Sep 2019, nearly doubled in just nine months. A staggering 96% were pornographic, and 99% of those mapped female celebrities’ faces onto porn stars. Danielle Citron, a Prof of law at the Boston University, says, “it appears that the deepfake technology is being weaponized against women mostly for the revenge.”

The fraud through deepfake audios, voice skins, and voice clones is also trending. Last March, the Chief of the UK subsidiary of a German energy firm paid nearly 2 00,000$ into a Hungarian bank account after being phoned by a fraudster who mimicked the German CEO’s voice. Similar scams have reportedly been used in recorded WhatsApp voice messages. 

How are Deepfakes created? 

Hyper-realistic or deepfake videos are the product of Artificial Intelligence applications that can combine, merge, replace, and superimpose images or short video clips for creating a fake video that appears authentic. The game-changing factor of deepfakes is the scope, scale, and sophistication of the technologies involved in its creation process.

Hyper-realistic or deepfake videos are the product of Artificial Intelligence applications that can combine, merge, replace, and superimpose images. Deepfakes
Hyper-realistic or deepfake videos are the product of Artificial Intelligence applications that can combine, merge, replace, and superimpose images.

It takes a few steps to make a face-swap video. First, runs thousands of face shots of the two people through an AI algorithm called an encoder. The encoder finds and learns similarities between the two faces and reduces them to their shared standard features, compressing the images in the process. A second AI algorithm called a decoder is then taught to recover the faces from the compressed images.

Because the faces are different, one needs to train one decoder to recover the first person’s face and another decoder to recover the second person’s face. To perform the face swap, he simply feeds encoded images into the “wrong” decoder. For example, a compressed image of person A’s face is fed into the decoder trained on person B. The decoder then reconstructs the face of person B with the expressions and orientation of face A. For a convincing video, this has to be done on every frame. 

How to spot Deepfakes? 

One of the biggest challenges of deepfakes is to find out how to counteract them knowing that the debunking methods’ development is always late regarding the production of misinformation. However, a great deal of effort has been made—and is still under the attempt to develop technology-based tools for detecting and correcting it, both from public and private organizations. 

Researchers from the Binghamton University, State University of New York, and Intel Corp have teamed up to develop a tool, “Deepfake catcher”, which boasts an accuracy rate above 90%. This tool works by analyzing the subtle differences in skin color caused by the human heartbeat. In deepfake, there is no consistency for the heartbeat and no pulse information at all. 

Another remarkable effort is the US’ Deepfake Detection Challenge (DFDC), co-partnered by AWS, Facebook, and AI’s media integrity steering committee US, to spur the researchers around the globe to build innovative technologies that can help detect manipulative media. 

There are specific rules one can follow to detect a deepfake video, such as facial transformation; checking if the eye is blinking as humanly as possible (weakness which has now been fixed in new videos), watching the eyes, scanning the cheek and forehead movements, etc. told Asad Makki, senior business solution manager at SAS.

This is how a layperson can detect manipulation in online content, but high-quality deepfakes aren’t easy to discern. The only way left is to educate and stay updated about advancements, both in deepfake tactics and detection technologies. Besides, one needs to build intuition for identifying “what is real and what is fake”. 

References:

UAE’s probe finds Auroras on the skies of the Red Planet

Scientia
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UAE’s probe finds Auroras on the skies of the Red Planet

A probe sent by the United Arab Emirates to study the Martian atmosphere has caught and brought us mesmerizing images of the beautiful natural light show i.e Auroras. The views on the red planet are as beautiful as, if not more, those on Earth.

Before the formal start of the Hope orbiter’s science mission, an instrument installed on the probe caught the aurora, which is known to be a phenomenon that is very difficult to study. The images were a delight, as they were not part of the planned observations on the mission.

Images released recently show the auroras standing out in the shape of bright structures set against the dark Martian night sky.

EMUS data showing the discrete aurora on Mars. The bright crescent marks the daylit side of the planet; the discrete aurora is the bright crackles seen on the nightside of Mars. (Image credit: Emirates Mars Mission)

In a statement given to Space.com reports: a space.com, Justin Deighan, a planetary scientist at the University of Colorado and deputy science lead of the mission, said, “They’re not easy to catch, and so that’s why seeing them basically right away with [Emirates Mars Mission] was kind of exciting and unexpected.” “It’s definitely something that was on our radar, so to speak, but just looking at our first set of nighttime data and saying, ‘Hey, wait a second — is that? — it can’t be — it is!’ — that was a lot of fun.” 

northern lights over snow-capped mountian
Aurora, the natural light display in the Earth’s sky, is predominantly seen in high-latitude regions.

The Ultraviolet Spectrometer installed on the probe was originally meant to study the massive halo of hydrogen and oxygen that surrounds the Red Planet, which eventually dissipates into open space.

“We did anticipate that the instrument would have the potential to do this,” Hessa Al Matroushi, the mission’s science lead, said in a statement. “It wasn’t designed to do it. But because we do have a mission that is targeting global coverage and we’re looking at Mars from different sides and very frequently within the atmosphere, that enabled us to have such a measurement of discrete auroras, which is very exciting.”

Also Read: JUTE: A PROMISING SOURCE OF NANOTECHNOLOGY DEVELOPMENTS FOR HUMAN WELFARES

Jute: A Promising Source of Nanotechnology Developments for Human Welfares

Syed Shaheen Shah
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Jute: A Promising Source of Nanotechnology Developments for Human Welfares

Technology is one of the essential driving components to economic growth at all levels. The recent advances in nanoscience and nanotechnology intend new and innovative applications in almost every aspect of life. At present, several groups around the globe are investing extensively in nanotechnology and considering it a powerful tool for the next industrial revolution. Nanotechnology harnesses the potential of the combination of physics, chemistry, material science, biotechnology, and engineering to create atomic-scale materials with a more significant number of exposed atoms on the surface. 

However, the success of nanotechnology partially depends on the source/precursor of nanomaterials or support of nanomaterials in terms of availability, renewability, functional groups, contamination-free, and biodegradability, etc.  These create more opportunities and prospects for Jute as an excellent precursor for the development of nanotechnology. 

Jute as a source of Nanotechnology

Jute is a type of Tiliaceae bast fiber and has a scientific name as Corchorus Capsularis since it is taken from corchor plants. Jute is one of the low-cost natural fibers and is now the most productive bast fiber. The main chemical composition of jute fibers and sticks, which have a trace amount of ash content, are cellulose, hemicellulose, and lignin, making Jute an ideal candidate for utilization in nanotechnology. 

Jute is an important natural fiber crop in the South Asian Zone next to cotton. It contributed robustly to countries’ economies; it was considered the “Golden Fiber.” Yet, the use of jute fibers has decreased because of the wide accessibility of long-lasting and fashionable synthetic fiber products in the market. Consequently, the scientists were drawn by the availability of vast amounts of unused, cheap, and environmental-friendly jute fibers and sticks for their utilization in nanotechnology. Its easy and endless availability also attracts Jute at a relatively low price. 

Recent developments of Nanotechnology via Jute

A group of experts, headed by Dr. Md. Abdul Aziz of King Fahd University of Petroleum & Minerals, Saudi Arabia, published a personal account entitled “Present Status and Future Prospects of Jute in Nanotechnology: A Review” (DOI: https://doi.org/10.1002/tcr.202100135) in a well-reputed journal (The Chemical Record; Quantile Rank: Q1 and Impact Factor: 6.77) of the Wiley publishing group. To cover a broader area of nanotechnology in an authentic way, the authors were selected from different research areas and are highly qualified in research and working in the leading world-class universities. They provided well-defined proofs of why Jute needs to be applied in nanotechnology with notable examples and prospects.

Jute is an important natural fiber crop.
Jute is an important natural fiber crop in the South Asian Zone next to cotton

They described the latest developments and future aspects of Jute in nanotechnology, including the preparation and applications of jute-derived nano-cellulose, as a scaffolder for other nanomaterials, catalysis, carbon preparation, life sciences, coatings, polymers, energy storage, drug delivery, fertilizer delivery, electrochemistry, reductant and stabilizer, petroleum industry, paper industry, polymeric nanocomposites, sensors, coatings, and electronics. 

These prospects will serve as a precursor of Jute-based nanotechnology research and industry setup in the future. The utilization of Jute (high cellulose-based biomass) in the nanomaterials area could bring prosperity and economic advantages, especially from an environmental perspective. The researchers aim to find out the economic and environmental benefits of Jute and highlighted various future aspects of Jute in nanotechnology.

Applications

Jute sticks derived carbon materials also played a vital role as electrode materials for electrochemical energy storage applications and showed better results than the commercially available activated carbon. (Shah et al., Jute Sticks Derived and Commercially Available Activated Carbons for Symmetric Supercapacitors with Bio‐electrolyte: A Comparative Study, Synthetic Metals, 277, 2021, 116765). 

The Jute-derived carbon-based supercapacitor delivered a higher specific capacitance (150 F/g) than the commercially available activated carbon-based supercapacitor (29 F/g). The developed supercapacitor illustrates an energy density of 20 Wh/kg at a power density of 500 W/kg with fabulous performance after 10,000 charges/discharge cycles. The research findings confirmed that the Jute bio-waste shows promise as an energy storage material. 

The utilization of Jute (high cellulose-based biomass) in the nanomaterials area could bring prosperity and economic advantages, especially from an environmental perspective.

There are two primary purposes of using Jute bio-waste in developing supercapacitors, i.e., it assists with waste disposal, i.e., utilizing waste to prepare energy storage materials and provides an economic platform for the sustainability of energy storage technology. 

The group members also summarized the Jute-derived carbon’s various preparation and utilization strategies (Aziz et al., Preparation and Utilization of Jute-Derived Carbon: A Short Review, The Chemical Record, 20, 2020, 1074-1098). Therefore, considering the technological, environmental, economic, and social contributions of Jute, it can be stated that Jute has a significant contribution in achieving sustainable developments. Therefore, increasing the productivity and benefits of Jute through promising input cost-saving technologies is a prime concern. It is very beneficial for farmers to focus on the cultivation of Jute and contribute to the recent technological developments.

Reference: “Present Status and Future Prospects of Jute in Nanotechnology: A Review” (DOI: https://doi.org/10.1002/tcr.202100135) in a well-reputed journal (The Chemical Record; Quantile Rank: Q1 and Impact Factor: 6.77)

Also, Read https://scientiamag.org/beating-the-odds-a-journey-from-buleda-to-cambridge/

The need for Quantum Technology in Pakistan

Yarjan Abdul Samad
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The need for Quantum Technology in Pakistan

Even though it is perfectly sensible for the education minister, Shafqat Mehmood, to be happy about the increase in the education budget, I along with several others who want to see Pakistan become a technologically advanced country would be happier and more content if some of the funds are utilized to develop a national research programme. The main aim of such a programme would be to lay the foundation for a thriving knowledge-based national economy. Such a programme should have been built decades ago to propel a Nanotechnology transformation through Industry-Academia collaborations in Pakistan. A few eminent educators, and scientists, did indeed put in some effort within this space, but we mostly missed out on the opportunity. But now a greater opportunity presents itself, and we really owe it to our future generations to ensure we do not let it slip through our hands again.

After Richard Feynman and other prominent scientists in the mid-1900s familiarised the world with nanotechnology, a new digital world was born. Whether it was millions of transistors on a single chip, or supercomputers processing gigabits of information in fractions of a second; it was predominantly nanotechnology that drove these advancements at an exponential pace. Nanotechnology has since matured and the world is now quickly moving towards the next breakthrough: Quantum Technology (also called Quantum Information Systems: QIS).

QIS or Quantum Technology is not really a new field of science as we have had its understanding for a few decades now. In fact, some of the theoretical frameworks for quantum technology was debated by the likes of Niels Bohr and Albert Einstein. Fast forward to today, the world is now ready for the different avenues QIS has to offer. QIS concerns the study, control, and manipulation of quantum systems with the goal of achieving information processing, and communication beyond the limits of the classical world of science. It is a multidisciplinary field, lying at the cusp of fields such as physics, mathematics, and engineering.

QIS is not in competition with areas such as Artificial Intelligence (AI), Machine Learning (ML), Advanced Robotics, and Digital Manufacturing, but can form strong foundations, which can further benefit these areas (e.g. quantum-enhanced AI and ML algorithms can further advance quantum computing capabilities). Thus, QIS along with augmenting AI and ML techniques have brought technology to a new and broader physical framework, providing fundamentally new capabilities. QIS technologies offer much more than just squeezing information into computers and multiplying the speeds of ubiquitous microchips and processors. It supports entirely new modes of computation with innovative and powerful algorithms based on quantum principles, which do not have any classical equivalents; rather they offer secure communications, simulation capabilities unattainable with classical devices, and systems with unparalleled sensitivity and precision.

The importance of QIS is the same, if not more, as it was of Nanotechnology a few decades ago which helped many countries rise to become developed states (e.g. Korea, China, Singapore). It is for this reason that leading countries in the world are spearheading projects to become future leaders in this field. For example, the European Union made an alliance of more than 5,000 scientists from all over Europe and launched a QIS project worth one billion euros, which makes it one of the three biggest research projects in the history of the EU. American and Chinese scientists are actively working on all aspects of QIS; from quantum computation, and communication to quantum metrology, sensing, and imaging. This is in addition to the billions of dollars of partnerships amongst leading companies (Amazon, Alibaba, Airbus, Google, IBM, Intel, etc.) with state-of-the-art research laboratories, and the top universities of the world.

In the race to advance QIS, developing countries are not an exception as many of them are pushing hard to build collaborations with leading Western, and Asian technology experts. For example, some developing countries such as India and to an extent Bangladesh have started active collaborations on QIS technology with material science, and electrical engineering departments of the University of Cambridge in the UK.  Through these collaborations, prominent researchers and professors from developing countries get to visit and work at leading universities and institutes which are already working on QIS technologies. This way they are able to learn and make scientific contributions, eventually bringing new knowledge back to their home countries.

I wanted to shed some light on the current state of affairs of QIS in Pakistan; however, the work so far is minimal and is widely dispersed. It is high time that a national research and development programme focusing on QIS is started in Pakistan which would involve the country’s leading universities, relevant private sector companies, and budding technology-focused start-ups. Through such a programme, we could also sign collaboration agreements with the prominent global universities and organizations working within this space. I suggest the following key aspects of QIS to be included in the programme:

  •  Micro & Nano Fabrication of Quantum devices
  • Quantum Communication and Quantum Control Systems
  • Quantum Metrology, Sensing & Imaging including for space technologies
  • Quantum Networks, among others

Both local Pakistani QIS experts, as well as those working abroad, can be utilized to help advise on detailed work packages with distinct short, medium, and long-term goals for each of the aforementioned aspects of QIS. This programme, if developed, should be done with swift timelines, challenging but realistic deliverables, and key performance indicators. I have no doubt, that if we bring together the intellectual wealth that we collectively possess as a nation, we will be able to advance rapidly within this new area of quantum technology.

The least that can be achieved with a national programme for QIS is short and long-term improvements in rankings of Pakistani universities, reinforced industry-academia collaborations, and better-skilled academics and graduates. And the ideal scenario would be a knowledge-based economy capable of building future technologies at home rather than being just a consumer nation.

Also Read: TALKING CLIMATE CHANGE, DISASTER MANAGEMENT, AND THE GEOLOGICAL STATE OF PAKISTAN WITH DR. QASIM JAN

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